Torso-mounted inertial sensors are typically attached at the waist and centered in the front or in the back in order to be closest to the center of gravity where there is less extraneous motion. Other mounting locations, such as in a vest pocket are feasible but they change the character of the motion signatures. Moving a system designed for waist mounting to another location on the body can cause performance issues depending on the motion models.
Torso-mounted inertial tracking systems that use microelectromechanical system (MEMS) sensors are typically developed as a pedometer based systems (though this is not always the case if additional velocity sensors are available to provide corrections).
The simplest of the pedometer type systems detects each step and uses a fixed predefined step length to compute the distance travelled, assuming all motions are walking or running forward. See, Judd, T. A Personal Dead Reckoning Module, in ION GPS. 1997. Kansas City, Mo. This type of system provides adequate performance for runners and other athletes with an approximately fixed pace attempting to record some measure of their workout distance.
Step detection is a critical function in any pedometer system. FIG. 1 shows typical raw z-axis accelerometer data from a waist mounted sensor for a person going up a series of steps. A circle mark at the bottom of each of the accelerometer signals indicates a heel strike associated with each step, which is based on a local minima in the accelerometer data. While one might expect that the magnitude of the signal would be consistent when performing uniform motions, it is clear from this sample that there can be significant magnitude variation in the acceleration while going up steps. As illustrated in FIG. 2, that variation can be even more extreme over different motions. If the variation in magnitude is significant, it can cause issues with missed detections because, for example, in order to eliminate false detections, values less than a threshold may be ignored. This may cause a simple detector to miss soft steps.
For example, FIG. 2 shows typical three axis accelerometer data taken while walking in an office building. The x-axis data is illustrated by a dash-dot-dot line, which is largely obscured by the y-axis data which is shown as a solid line. The z-axis data is illustrated by a dash-dash line. The data collected and illustrated in FIG. 2 was collected while a subject walked down 4 flights of stairs, down a hallway, and up 4 flights of stairs. A visual inspection of this accelerometer data suggests the ability to differentiate between walking down stairs, upstairs and forward based on signal characteristics, as indicated in FIG. 2.
More sophisticated pedometers include motion models to better estimate step length. In the context of pedestrian tracking, the motion models typically referred to in the literature describe motion type (walking, running, crawling . . . ) and step length and frequency. See, id.; Funk, B., et al., Method and System for Locating and Monitoring First Responders, U.S. Publication Number 2008/0077326 (“Funk”).
For example, step length can be estimated based on a tracked subject's height, step frequency, and other factors. In general, for walking, the speed and step length increase when the step frequency increases, and for a given step frequency, step length remains fairly constant (with some distribution about a nominal value). Considering the human body's locomotion and physical restrictions, different methods have been proposed to approximate the step length. Linear models have been derived by fitting a linear combination of step frequency and measured acceleration magnitude to the captured data. Pedometer systems may also provide a mechanism for using GPS or other measures to adaptively update the step length estimates. See, Ladetto, Q., On foot navigation: continuous step calibration using both complementary recursive prediction and adaptive Kalman filtering, in ION GPS. 2000; Lee, S. and K. Mase, Recognition of Walking Behaviors for Pedestrian Navigation, in IEEE Conference on Control Applications (CCA01). 2001: Mexico City, Mexico; Fang, L., et al., Design of a Wireless Assisted Pedestrian Dead Reckoning System—The NavMote Experience. IEEE Transactions on Instrumentation and Measurement, 2005. 54(6): p. 2342-2358; Ladetto, Q., et al. Digital Magnetic Compass and Gyroscope for Dismounted Solider Position and Navigation, in Military Capabilities enabled by Advances in Navigation Sensors, Sensors & Electronics Technology Panel, NATO-RTO meetings. 2002. Istanbul, Turkey (“Ladetto”); Godha, S., G. Lachapelle, and M. Cannon, Integrated GPS/INS System for Pedestrian Navigation in a Signal Degraded Environment. in ION GNSS. 2006. Fort Worth, Tex.: ION.
In Chau, T., A Review of Analytical Techniques for Gait Data. Part 1: Fuzzy, Statistical and Fractal Methods. Gait and Posture, 2001. 13: p. 49-66 and Chau, T., A Review of Analytical Techniques for Gait Data. Part 2: Neural Network and Wavelet Methods. Gait and Posture, 2001. 13: p. 102-120, a review of analytical techniques is presented. The techniques have the potential for a step data analysis, including Fuzzy Logic (FL), statistical, fractal, wavelet, and Artificial Neural Network (ANN) methods.
In order to account for motion direction, pedometers may break the tracking problem down into motion classification and then scaling, not assuming, for example, that every motion is forward. They provide a mechanism to classify the motions as forward, backward, up, down, left, right, etc. See. Funk; Ladetto; and Soehren, W. and W. Hawkinson, Prototype Personal Navigation System. IEEE A&E SYSTEMS MAGAZINE, 2008 (April) (“Soehren”). While prior claims have been made regarding the ability to classify motion based on comparison with stored motion data or to use neural networks to classify motion, little detail, and certainly not enabling disclosures have been provided regarding how this is done. Aside from the use of vision systems for classification, published work on motion classification is limited. Ladetto suggests using the antero-posterior acceleration divided by the lateral acceleration as an indicator of direction together with the lateral acceleration data peak angles to determine left versus right side stepping. Soehren uses an abrupt change in step frequency to detect walking versus running. Funk describes a neural network classification method where sensor data is segmented into steps and then normalized (re-sampled) to make a consistent number of inputs to the neural network classifier that is independent of step frequency. This method has been used to classify standard pedestrian motions as well as more utilitarian job related motions such as crawling and climbing ladders.